Recent Advancements and Challenges of Turkic Central Asian Language Processing
It addresses challenges in NLP for low-resource languages like Kazakh and Uzbek, but is incremental as it primarily reviews existing work.
The paper summarizes recent progress in natural language processing for low-resource Central Asian Turkic languages, highlighting advancements in dataset collection and model development, but does not report specific numerical results.
Research in NLP for Central Asian Turkic languages - Kazakh, Uzbek, Kyrgyz, and Turkmen - faces typical low-resource language challenges like data scarcity, limited linguistic resources and technology development. However, recent advancements have included the collection of language-specific datasets and the development of models for downstream tasks. Thus, this paper aims to summarize recent progress and identify future research directions. It provides a high-level overview of each language's linguistic features, the current technology landscape, the application of transfer learning from higher-resource languages, and the availability of labeled and unlabeled data. By outlining the current state, we hope to inspire and facilitate future research.